Gastos Casa
22 April, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 4/3/2024 | Comida | 6000 | Andrés | pasas |
| 5/3/2024 | Uber cumple papá | 8582 | Tami | NA |
| 6/3/2024 | Agua | 16549 | Andrés | NA |
| 7/3/2024 | Enceres | 4645 | Andrés | descuentos desodorantes |
| 10/3/2024 | Comida | 7470 | Andrés | NA |
| 10/3/2024 | Comida | 90504 | Tami | Supermercado |
| 10/3/2024 | Diosi | 21081 | Andrés | pipeta |
| 11/3/2024 | Diosi | 66970 | Andrés | n&d 50990 + lavanda 3asy clean 10k 79990x2 |
| 17/3/2024 | Comida | 55951 | Tami | Supermercado |
| 19/3/2024 | VTR | 21990 | Andrés | NA |
| 24/3/2024 | Comida | 94384 | Tami | Supermercado |
| 27/3/2024 | Comida | 27980 | Tami | Barras Wild Soul |
| 27/3/2024 | Electricidad | 56338 | Andrés | NA |
| 29/3/2024 | Comida | 69144 | Tami | Supermercado |
| 1/4/2024 | Comida | 11990 | Andrés | vino |
| 1/4/2024 | Comida | 21300 | Andrés | piwen |
| 2/4/2024 | Comida | 34980 | Tami | bar providencia |
| 3/4/2024 | Comida | 30969 | Tami | Supermercado |
| 5/4/2024 | Enceres | 24990 | Tami | Shampoo |
| 5/4/2024 | Enceres | 16600 | Tami | Acondicionador cabello |
| 6/4/2024 | Enceres | 16600 | Andrés | Acondicionador cabello |
| 7/4/2024 | Comida | 33828 | Tami | Supermercado |
| 10/4/2024 | Comida | 20230 | Tami | Supermercado |
| 12/4/2024 | Comida | 7000 | Andrés | NA |
| 13/4/2024 | Comida | 22800 | Andrés | empanadas |
| 14/4/2024 | Comida | 75094 | Tami | Supermercado |
| 18/4/2024 | Parafina | 49376 | Tami | NA |
| 19/4/2024 | VTR | 21990 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 7.9536e+08 2 7.6048 5e-04 ***
## lag_depvar 1.0342e+11 1 1977.6963 <2e-16 ***
## Residuals 3.6448e+10 697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 910.7709 13546.91 0.0201442
## 2-0 29062.362 23340.9616 34783.76 0.0000000
## 2-1 21833.524 18480.8933 25186.16 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 277 63285.29 2 63044.86
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## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
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## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 55525.00 2 52725.00
## 695 44413.00 2 55525.00
## 696 37200.14 2 44413.00
## 697 30212.43 2 37200.14
## 698 26456.71 2 30212.43
## 699 24716.71 2 26456.71
## 700 31020.29 2 24716.71
## 701 32132.57 2 31020.29
## 702 32902.57 2 32132.57
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 545 51296.62 15365.008
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 55525.00
## [694] 44413.00 37200.14 30212.43 26456.71 24716.71 31020.29 32132.57
## [701] 32902.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1946.45276 4009.33353 -507.22691 2464.81139 -2908.63762 540.91572
## 8 9 10 11 12 13
## -5623.25234 -1224.50025 -4006.64994 -497.06169 -5007.51239 -1724.68093
## 14 15 16 17 18 19
## -1014.26436 272.64311 -3323.03543 -482.43361 -2219.58333 6505.57759
## 20 21 22 23 24 25
## -1525.07678 -1217.49601 1458.84693 -1175.95017 235.51215 1705.35313
## 26 27 28 29 30 31
## -7064.87300 896.70100 8166.72249 506.45652 75.19003 -2316.07365
## 32 33 34 35 36 37
## 1626.35292 4643.07475 1253.35254 2523.00738 -1715.91418 4723.91866
## 38 39 40 41 42 43
## 4372.16877 -2160.45534 -2908.99113 -1084.06694 -10732.16977 7161.80143
## 44 45 46 47 48 49
## 2538.76317 1383.66470 8137.75270 817.98284 6653.23731 6906.54521
## 50 51 52 53 54 55
## -5628.68842 -4647.79437 -4990.93396 -7932.03030 6026.82032 -4088.33454
## 56 57 58 59 60 61
## -4955.71347 3740.64438 836.61532 -65.08403 113.54315 -5019.15626
## 62 63 64 65 66 67
## 18044.11250 3798.81324 -3461.15876 6041.36604 7521.18632 14887.48597
## 68 69 70 71 72 73
## 2096.78858 -12837.84715 -1145.31278 4768.19504 -4731.79490 -4318.71475
## 74 75 76 77 78 79
## -10477.55158 2352.11215 -5467.99159 936.55456 -6963.23034 376.03660
## 80 81 82 83 84 85
## -2496.25855 -2846.74468 -4098.82214 -732.40670 2138.21790 3638.43897
## 86 87 88 89 90 91
## 416.46869 -530.80049 150.59435 4264.74373 -1140.58820 1156.28652
## 92 93 94 95 96 97
## -2044.61833 -1052.48825 157.78172 260.28419 -7492.67670 2290.27440
## 98 99 100 101 102 103
## -8660.31452 -3098.99849 -4214.20120 -1939.55336 -1459.63275 2992.58323
## 104 105 106 107 108 109
## -2465.80411 2456.98304 -1244.77627 880.99043 2521.77383 -3178.21679
## 110 111 112 113 114 115
## -4783.08137 -961.68353 1796.08426 11624.32228 -1154.89469 2730.07042
## 116 117 118 119 120 121
## 4350.92433 3634.10658 -940.21956 -4589.98419 -3672.55656 2318.47639
## 122 123 124 125 126 127
## -1703.82413 1344.41183 8879.31789 978.00732 256.19050 -2409.08409
## 128 129 130 131 132 133
## 2721.92321 7145.11890 1183.04688 -8336.89671 1784.52502 4189.08716
## 134 135 136 137 138 139
## -3064.30930 -1371.98424 -829.55047 -3868.81105 1144.49046 -513.59819
## 140 141 142 143 144 145
## -2935.06400 1663.26281 -1906.74735 -7874.86023 1901.44606 -3573.96949
## 146 147 148 149 150 151
## 1976.52473 -340.23578 948.00304 -411.40912 1302.66767 1160.96076
## 152 153 154 155 156 157
## 3349.66290 -4824.92341 -1203.04739 -3275.02848 5882.18970 9757.25284
## 158 159 160 161 162 163
## -3477.97183 -4848.31527 3496.23153 159.57843 2681.27199 -5876.47578
## 164 165 166 167 168 169
## -6778.70260 4057.98796 17371.25901 3832.56211 -163.62170 -2233.92419
## 170 171 172 173 174 175
## -937.06864 3734.69752 -46.69195 -7908.05881 2922.64925 4423.47635
## 176 177 178 179 180 181
## 775.36133 8900.25528 -8999.75594 -3356.04051 -10674.63537 -11298.80540
## 182 183 184 185 186 187
## 1057.85139 9165.06453 -1417.81811 5932.73704 6640.83519 13320.91415
## 188 189 190 191 192 193
## 8738.29892 -3687.48618 2749.82068 10652.70213 -1266.61000 -2129.52419
## 194 195 196 197 198 199
## -10028.70791 -6258.97179 1256.64173 -5190.30881 -9812.06968 5262.61643
## 200 201 202 203 204 205
## -3100.14040 -1767.11058 -862.64132 6443.08652 9921.15351 734.02614
## 206 207 208 209 210 211
## 3073.59527 3268.68433 5976.23522 13075.40603 -5319.83867 -11035.24493
## 212 213 214 215 216 217
## -5563.26622 -10555.26616 -5161.89557 1401.51298 -13092.12346 16176.12633
## 218 219 220 221 222 223
## 7823.67368 1639.16307 26810.19850 12947.42426 7851.88392 14567.07386
## 224 225 226 227 228 229
## -3275.29953 -1224.71897 4213.14310 790.42237 3133.06967 9382.25142
## 230 231 232 233 234 235
## 6274.15258 -1440.62256 -1434.88253 9756.12108 -11101.88793 -7055.11208
## 236 237 238 239 240 241
## -8418.87577 -10086.14057 2981.85571 1313.70497 -8305.63284 -9084.38198
## 242 243 244 245 246 247
## 8916.95385 -7805.34778 2371.42708 -10364.15170 -4219.21124 1240.85196
## 248 249 250 251 252 253
## 869.99786 -12411.54411 3426.31197 1924.07003 4123.15272 2113.79526
## 254 255 256 257 258 259
## -1146.80975 11145.12742 21019.76083 3559.09194 -3916.01240 4374.70197
## 260 261 262 263 264 265
## -1412.24917 3968.76955 -4602.56765 -10726.38882 -4700.75435 -544.75994
## 266 267 268 269 270 271
## -5207.91151 8708.40993 -4237.09956 4183.79992 -2060.10120 4452.60410
## 272 273 274 275 276 277
## 781.80454 7378.27306 -1261.92757 12143.12957 -4347.02837 1882.41892
## 278 279 280 281 282 283
## -214.49063 7987.01761 -4851.24056 -2604.09925 -11176.42130 -2712.23352
## 284 285 286 287 288 289
## 18594.42173 7937.07946 2954.15170 -405.63879 1095.41970 6575.10733
## 290 291 292 293 294 295
## 7107.04321 -18501.43831 -11097.99094 -8196.16035 9522.99040 3059.01901
## 296 297 298 299 300 301
## -1151.10778 27420.51822 10373.27307 5276.48689 9898.29888 3289.12265
## 302 303 304 305 306 307
## -624.00000 8243.82766 -23907.82893 -3442.66659 -123.38520 -6916.27244
## 308 309 310 311 312 313
## -3988.93746 2886.89948 -9191.06160 -3311.73931 -8277.52660 1416.10024
## 314 315 316 317 318 319
## -3251.92288 1941.12634 -4143.29733 27362.12085 -529.86184 3456.93282
## 320 321 322 323 324 325
## 11012.08151 5860.21546 32676.44629 5698.91242 -20369.75225 2081.24875
## 326 327 328 329 330 331
## 1385.92766 -6207.82666 -1567.93281 -33132.10083 714.78050 -2417.36586
## 332 333 334 335 336 337
## -194.94842 -3236.56938 4014.96716 -439.01516 -6939.66020 -3153.87759
## 338 339 340 341 342 343
## -2234.43022 -7717.87283 3764.11046 -1390.43396 -1748.58194 -1000.79694
## 344 345 346 347 348 349
## 180.23661 505.32874 -1575.13358 -9406.88367 -13248.92590 2167.89544
## 350 351 352 353 354 355
## -4404.91045 -3751.33973 -6075.77142 1630.16412 1315.27227 2723.87855
## 356 357 358 359 360 361
## -3749.65094 -521.58632 683.97617 7041.52044 381.29755 69.36488
## 362 363 364 365 366 367
## 2690.26948 -2618.87080 -773.78976 -8645.63052 -4603.98227 -6216.88241
## 368 369 370 371 372 373
## -4990.84433 -7313.84232 4915.39968 348.69326 7121.12254 -7553.75441
## 374 375 376 377 378 379
## -2243.60448 -3373.02878 -2465.90058 -12459.57772 1807.16424 -10679.98706
## 380 381 382 383 384 385
## 5579.10903 9304.99522 3199.78478 -2291.75664 1690.42826 6847.50800
## 386 387 388 389 390 391
## 11573.98171 -5548.63753 -5188.60572 -45.68971 8669.62551 1993.63799
## 392 393 394 395 396 397
## 11401.23923 -9616.17618 2917.69000 869.40669 712.21547 -511.09270
## 398 399 400 401 402 403
## -437.11922 -14374.49694 8508.09553 -1100.36487 -1298.35683 7048.78886
## 404 405 406 407 408 409
## -7799.08518 -1235.63510 -2472.10414 -5771.02269 -2847.98058 -3911.65745
## 410 411 412 413 414 415
## -8763.76939 6071.12081 1659.66946 -7324.28543 -7694.80231 14172.20727
## 416 417 418 419 420 421
## 3912.28278 4620.00090 -7874.85517 -4659.49449 -2550.49978 2860.51818
## 422 423 424 425 426 427
## -13932.81459 -2825.65174 -9131.11434 2928.07626 6950.54452 6630.66415
## 428 429 430 431 432 433
## -3866.90765 -4033.79333 -4663.91741 -1760.56182 -5682.47357 -6630.78479
## 434 435 436 437 438 439
## -5990.18014 -1459.02768 -895.70061 -5003.22861 2534.66814 4841.27727
## 440 441 442 443 444 445
## -4994.40660 -2129.23172 1602.33134 -3781.60611 2869.59371 -6504.82667
## 446 447 448 449 450 451
## -12085.04002 -4574.89447 9572.49955 -1988.52644 4794.86396 -5772.19648
## 452 453 454 455 456 457
## -1069.69872 440.72586 3099.68432 -12157.84941 3376.26124 -6643.94972
## 458 459 460 461 462 463
## 6533.14433 3100.72903 2630.41741 -3697.12429 2206.85003 129.84646
## 464 465 466 467 468 469
## 1931.15980 -366.34419 3498.95766 -2461.79227 5954.76143 -6741.07670
## 470 471 472 473 474 475
## -2831.20746 -2094.45005 -4564.99636 3062.97441 7903.89799 -5834.02875
## 476 477 478 479 480 481
## 1610.16039 -6034.73297 -2752.89006 2088.35787 -12823.14515 -9757.58126
## 482 483 484 485 486 487
## -1270.06537 -25.84892 -978.72206 -1341.93577 -9574.15542 11039.81052
## 488 489 490 491 492 493
## 6311.11583 7564.26882 -5223.83378 5522.85655 9497.06751 6334.49627
## 494 495 496 497 498 499
## -13156.79487 -10393.65449 -3365.59184 -1048.46259 -461.38511 -7552.94008
## 500 501 502 503 504 505
## 628.74828 4332.84326 5610.50110 821.56523 248.48896 -7070.51189
## 506 507 508 509 510 511
## 672.59998 -4930.87958 1911.49324 -1187.75362 -8052.31037 -561.99722
## 512 513 514 515 516 517
## -2621.38874 -542.47395 1389.80322 -9409.50193 -7754.91677 24247.74031
## 518 519 520 521 522 523
## 10037.84482 6174.30708 -5006.59845 3054.20973 17286.36513 11879.52700
## 524 525 526 527 528 529
## -23675.31342 -4852.19872 -3579.01703 4692.93628 -189.30475 -10940.32899
## 530 531 532 533 534 535
## 4451.94357 14024.23715 -4729.77344 4561.49156 5777.01110 -1528.54406
## 536 537 538 539 540 541
## -4311.51209 -6896.78763 -1990.18052 8421.21603 310.56909 -7960.52218
## 542 543 544 545 546 547
## 1919.84714 -470.77807 494.39048 -10894.49009 -11021.96088 1997.30729
## 548 549 550 551 552 553
## 7008.58510 -1225.68723 927.33288 -7611.85830 8611.48305 1048.51644
## 554 555 556 557 558 559
## -11791.02780 9207.56262 8802.64530 324.03602 5068.68571 -3323.71938
## 560 561 562 563 564 565
## 14312.33541 21822.85273 -5973.32733 -9296.60694 7037.09204 533.23671
## 566 567 568 569 570 571
## 3740.74297 -7079.53788 -17097.63350 6710.10775 6560.61003 2093.02942
## 572 573 574 575 576 577
## 3302.99093 1997.54020 -1929.91998 14922.56643 -9315.81600 -6025.26872
## 578 579 580 581 582 583
## 8864.92876 3092.64977 -6295.77139 7686.19038 -3555.95865 -2577.97993
## 584 585 586 587 588 589
## 15870.21140 -14188.71674 8583.94180 304.62772 -5987.17710 -593.67003
## 590 591 592 593 594 595
## 407.33759 -10492.93832 1861.52603 -7047.15666 3110.76264 8956.91316
## 596 597 598 599 600 601
## -7316.47009 5965.04822 2910.98715 7056.56794 -2930.24787 6367.79568
## 602 603 604 605 606 607
## -8029.65848 2434.47616 1469.80685 3346.74909 1728.40685 636.77390
## 608 609 610 611 612 613
## -5575.91974 8248.07494 -934.30000 -2344.86402 -3253.87529 -8062.32372
## 614 615 616 617 618 619
## 12051.52943 5154.38809 -9054.70273 11789.23622 6325.25840 -5252.98307
## 620 621 622 623 624 625
## 26610.02660 -12333.42714 -6447.90700 3406.97668 -3886.87305 -10369.57900
## 626 627 628 629 630 631
## 11415.43940 -21398.43969 -2390.15966 8700.91495 11268.15817 -1304.43717
## 632 633 634 635 636 637
## 33510.68258 -6045.61743 6148.41679 5853.14261 -1797.95581 -4933.58018
## 638 639 640 641 642 643
## -1608.86639 -12136.57499 -2080.77209 -1741.06696 -2385.78042 -2738.68356
## 644 645 646 647 648 649
## 1918.04804 4564.85902 17150.61890 18903.95244 1391.13896 5261.57799
## 650 651 652 653 654 655
## 11091.91089 20694.24170 1440.18071 -27425.59305 -1034.15793 -2008.52279
## 656 657 658 659 660 661
## 2121.06459 -2923.75179 -10390.03629 1790.72595 4382.90940 -798.37436
## 662 663 664 665 666 667
## 13233.11446 1695.26667 2147.26052 -11353.58790 1583.56805 1409.18219
## 668 669 670 671 672 673
## -4930.68847 -7227.12343 2181.90680 -3558.67179 2799.89610 -3210.30175
## 674 675 676 677 678 679
## -9193.60719 -8247.93060 -2984.54556 164.77210 2871.61703 794.29636
## 680 681 682 683 684 685
## -3718.35557 -1724.17311 -1233.71070 -8152.54003 4670.26295 -2144.64503
## 686 687 688 689 690 691
## -1306.64872 682.62113 10972.86725 10098.29370 10975.10147 -9212.08214
## 692 693 694 695 696 697
## -3230.77484 -2864.03680 3009.39361 -10513.91197 -8157.33068 -8933.47258
## 698 699 700 701 702
## -6671.50314 -5177.15499 2624.87062 -1691.35686 -1879.23570
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17322.83 20129.67 24323.37 24045.33 26365.35 23735.80 24441.97 19741.64
## 10 11 12 13 14 15 16 17
## 19481.94 16862.35 17628.80 14404.54 14454.98 15110.21 16782.75 15126.58
## 18 19 20 21 22 23 24 25
## 16146.58 15528.99 22511.08 21608.07 21095.30 22958.52 22294.06 22937.36
## 26 27 28 29 30 31 32 33
## 24757.16 18771.58 20473.28 28199.54 28256.38 27933.93 25596.93 26979.50
## 34 35 36 37 38 39 40 41
## 30768.08 31111.56 32500.77 30046.65 34070.83 37233.46 34331.28 31187.35
## 42 43 44 45 46 47 48 49
## 30051.46 20764.48 28176.67 30578.62 31652.39 38393.59 37895.33 42491.45
## 50 51 52 53 54 55 56 57
## 46667.69 39469.08 34114.51 29207.74 22449.32 28650.19 25279.28 21629.36
## 58 59 60 61 62 63 64 65
## 25975.24 27216.94 27509.74 27915.73 23845.17 40201.33 42019.16 37332.49
## 66 67 68 69 70 71 72 73
## 41479.81 46325.80 56842.78 54884.70 40337.03 37878.23 40853.37 35234.29
## 74 75 76 77 78 79 80 81
## 30750.98 21586.17 24742.28 20725.73 22782.23 17750.11 19736.97 18974.46
## 82 83 84 85 86 87 88 89
## 18015.96 16112.26 17371.92 20928.85 25283.96 26259.80 26284.41 26892.40
## 90 91 92 93 94 95 96 97
## 30959.02 29806.14 30791.33 28883.20 28094.36 28457.29 28858.11 22526.58
## 98 99 100 101 102 103 104 105
## 25498.89 18628.14 17500.49 15568.98 15864.49 16532.27 20941.52 20038.02
## 106 107 108 109 110 111 112 113
## 23499.35 23292.30 24944.65 27780.65 25314.22 21808.11 22079.63 24688.39
## 114 115 116 117 118 119 120 121
## 35398.89 33617.36 35428.79 38384.61 40312.79 38033.98 32928.41 29321.67
## 122 123 124 125 126 127 128 129
## 31374.97 29679.30 30844.11 38336.14 37983.67 37058.51 33966.51 35722.45
## 130 131 132 133 134 135 136 137
## 41043.81 40492.04 31818.47 33065.34 36209.88 32671.41 31081.55 30179.53
## 138 139 140 141 142 143 144 145
## 26785.37 28179.74 27952.64 25671.74 27667.46 26311.72 20004.55 22992.11
## 146 147 148 149 150 151 152 153
## 20849.62 23784.52 24316.85 25884.69 26064.19 27694.90 28977.19 31966.35
## 154 155 156 157 158 159 160 161
## 27500.76 26774.17 24364.10 30174.60 41498.40 39852.32 37254.63 42203.71
## 162 163 164 165 166 167 168 169
## 43592.30 46959.76 42489.99 37863.73 43212.03 59283.01 61463.76 59900.35
## 170 171 172 173 174 175 176 177
## 56771.07 55193.02 57857.26 56895.20 49296.64 52080.10 55769.64 55805.32
## 178 179 180 181 182 183 184 185
## 62833.04 53470.04 50267.06 41206.09 32865.43 36323.94 46284.10 45747.83
## 186 187 188 189 190 191 192 193
## 51616.16 57279.66 67909.70 73117.63 66901.75 67092.44 74062.47 69800.24
## 194 195 196 197 198 199 200 201
## 65386.57 54782.97 48897.79 50301.88 45959.07 38238.95 44572.57 42825.11
## 202 203 204 205 206 207 208 209
## 42468.21 42939.77 49637.42 58400.55 58035.40 59735.74 61368.05 65105.45
## 210 211 212 213 214 215 216 217
## 74437.70 66632.82 54989.41 49674.69 40798.75 37799.63 40869.12 31030.87
## 218 219 220 221 222 223 224 225
## 47763.61 54980.55 55869.66 78312.15 85700.83 87675.64 95159.30 86238.58
## 226 227 228 229 230 231 232 233
## 80322.14 79910.01 76607.50 75780.89 80450.70 81795.62 76310.03 71590.88
## 234 235 236 237 238 239 240 241
## 77164.32 64001.54 56151.02 48215.85 39946.43 44078.87 46201.06 39744.67
## 242 243 244 245 246 247 248 249
## 33513.90 43650.49 37979.00 41858.87 34232.50 32956.72 36560.14 39343.97
## 250 251 252 253 254 255 256 257
## 30303.55 36157.36 39904.85 45025.92 47705.67 47205.44 57360.24 74609.19
## 258 259 260 261 262 263 264 265
## 74426.87 67832.44 69293.25 65567.66 66993.28 60839.53 50266.33 46350.05
## 266 267 268 269 270 271 272 273
## 46556.48 42718.45 51397.67 47723.63 51811.53 49954.82 53964.48 54256.30
## 274 275 276 277 278 279 280 281
## 60188.36 57856.16 67391.89 61402.87 61609.92 59982.41 65643.81 59463.24
## 282 283 284 285 286 287 288 289
## 56075.85 45776.38 44195.86 61183.63 66635.28 67038.92 64493.15 63593.46
## 290 291 292 293 294 295 296 297
## 67537.67 71392.44 52658.56 42901.02 36997.01 47171.98 50367.82 49494.34
## 298 299 300 301 302 303 304 305
## 73347.44 79208.51 79866.70 84413.73 82637.86 77738.60 81156.26 56411.10
## 306 307 308 309 310 311 312 313
## 52725.24 52409.56 46287.79 43536.81 47089.06 39746.88 38487.10 33125.76
## 314 315 316 317 318 319 320 321
## 36856.64 36049.59 39826.73 37839.74 63260.43 61132.21 62732.78 70617.50
## 322 323 324 325 326 327 328 329
## 72970.98 98091.37 96492.04 72664.89 71479.79 69860.40 61926.22 59089.24
## 330 331 332 333 334 335 336 337
## 29463.65 33098.94 33532.23 35819.28 35169.46 40854.73 41915.09 37230.02
## 338 339 340 341 342 343 344 345
## 36455.57 36580.44 31965.75 37879.72 38533.72 38788.51 39651.91 41412.53
## 346 347 348 349 350 351 352 353
## 43208.71 42963.88 36008.50 26709.96 31978.91 30856.05 30451.91 28102.12
## 354 355 356 357 358 359 360 361
## 32714.73 36415.84 40816.22 39030.87 40273.31 42381.48 49671.99 50214.78
## 362 363 364 365 366 367 368 369
## 50413.59 52841.87 50360.93 49813.34 42562.70 39799.17 36030.27 33840.41
## 370 371 372 373 374 375 376 377
## 29954.03 37138.74 39393.31 47167.18 41224.18 40679.17 39237.19 38776.58
## 378 379 380 381 382 383 384 385
## 29773.55 34306.56 27456.61 35559.58 45746.36 49261.33 47559.14 49522.63
## 386 387 388 389 390 391 392 393
## 55654.73 65005.92 58313.32 52859.83 52592.37 59867.50 60383.48 68929.46
## 394 395 396 397 398 399 400 401
## 58189.31 59734.02 59300.36 58791.52 57299.83 56078.93 43024.90 51489.08
## 402 403 404 405 406 407 408 409
## 50503.64 49484.50 55795.23 48443.21 47764.10 46114.45 41852.84 40700.09
## 410 411 412 413 414 415 416 417
## 38791.34 32969.02 40730.47 43615.43 38363.09 33520.79 48182.15 51972.57
## 418 419 420 421 422 423 424 425
## 55846.28 48421.92 44797.21 43491.91 47027.67 35610.51 35343.54 29683.50
## 426 427 428 429 430 431 432 433
## 35194.31 43404.19 50198.91 47010.08 44120.20 41088.85 40978.62 37506.21
## 434 435 436 437 438 439 440 441
## 33699.18 30972.31 32526.13 34349.37 32382.19 37179.58 43297.41 40095.66
## 442 443 444 445 446 447 448 449
## 39805.81 42769.75 40685.69 44618.83 39932.90 31091.89 29945.79 41142.24
## 450 451 452 453 454 455 456 457
## 40828.28 46399.63 42097.41 42442.13 44039.74 47705.42 37722.74 42503.52
## 458 459 460 461 462 463 464 465
## 37991.43 45453.56 48923.87 51507.41 48283.15 50590.87 50789.55 52511.92
## 466 467 468 469 470 471 472 473
## 52016.61 54918.79 52284.81 57264.65 50619.78 48264.45 46870.57 43542.60
## 474 475 476 477 478 479 480 481
## 47245.67 54603.60 49109.27 50788.45 45650.89 44052.78 46845.72 36409.44
## 482 483 484 485 486 487 488 489
## 30061.92 31904.85 34563.44 36032.36 36984.58 30715.19 43068.46 49634.59
## 490 491 492 493 494 495 496 497
## 56368.41 51154.57 55919.36 63445.22 67202.79 53653.23 44364.16 42417.03
## 498 499 500 501 502 503 504 505
## 42735.67 43515.65 38080.25 40445.30 45671.93 51273.29 51972.94 52081.94
## 506 507 508 509 510 511 512 513
## 45872.83 47193.88 43505.94 46222.47 45892.88 39697.43 40812.53 39999.33
## 514 515 516 517 518 519 520 521
## 41089.34 43692.07 36633.35 31979.40 55531.58 63576.98 67178.31 60650.93
## 522 523 524 525 526 527 528 529
## 61971.49 75365.19 82243.31 57547.48 52490.02 49231.06 53548.16 53061.47
## 530 531 532 533 534 535 536 537
## 43383.77 48305.05 60786.63 55384.94 58734.56 62665.97 59760.23 54861.22
## 538 539 540 541 542 543 544 545
## 48415.89 47090.78 54915.72 54669.67 47334.87 49527.06 49356.18 50040.20
## 546 547 548 549 550 551 552 553
## 40821.39 32772.55 37052.99 45054.83 44854.67 46536.43 40630.95 49516.48
## 554 555 556 557 558 559 560 561
## 50655.46 40579.15 49985.21 57736.82 57110.74 60657.58 56484.66 68078.86
## 562 563 564 565 566 567 568 569
## 84531.47 74762.61 63487.91 67844.62 65995.54 67165.40 58854.63 43070.18
## 570 571 572 573 574 575 576 577
## 49979.68 55801.26 56967.29 59013.46 59651.35 56818.43 68891.82 58415.55
## 578 579 580 581 582 583 584 585
## 52227.36 59721.35 61204.06 54395.81 60573.67 56212.41 53298.79 66676.86
## 586 587 588 589 590 591 592 593
## 52311.63 59551.94 58657.18 52468.24 51783.23 52055.37 42902.62 45659.87
## 594 595 596 597 598 599 600 601
## 40362.38 44548.09 53187.33 46612.95 52389.01 54733.15 60321.96 56534.49
## 602 603 604 605 606 607 608 609
## 61280.09 52968.10 54821.48 55586.82 57862.31 58428.23 57975.49 52235.35
## 610 611 612 613 614 615 616 617
## 59197.01 57284.58 54422.88 51175.61 44238.18 55585.47 59417.85 50481.62
## 618 619 620 621 622 623 624 625
## 60736.31 64861.98 58443.97 80356.71 65690.19 58128.17 60102.73 55521.86
## 626 627 628 629 630 631 632 633
## 45994.13 56549.87 37381.59 37243.80 46676.56 57010.72 55083.03 83405.05
## 634 635 636 637 638 639 640 641
## 73730.30 75899.86 77513.96 72315.01 65137.44 61819.43 49895.77 48287.21
## 642 643 644 645 646 647 648 649
## 47194.49 45698.25 44105.81 46744.71 51296.67 66055.33 80275.15 77439.28
## 650 651 652 653 654 655 656 657
## 78330.23 84118.47 97372.53 92205.45 62897.02 60384.95 57382.51 58353.18
## 658 659 660 661 662 663 664 665
## 54844.61 45393.27 47743.80 52000.37 51204.03 62601.88 62481.31 62766.73
## 666 667 668 669 670 671 672 673
## 51385.86 52726.10 53730.12 49134.98 43200.09 46191.96 43824.82 47262.16
## 674 675 676 677 678 679 680 681
## 45046.46 37985.64 32719.40 32716.94 35426.95 40091.85 42320.21 40353.03
## 682 683 684 685 686 687 688 689
## 40376.28 40818.68 35241.31 41480.93 40985.51 41280.52 43247.70 53803.56
## 690 691 692 693 694 695 696 697
## 62140.90 70075.94 59524.63 55589.04 52515.61 54926.91 45357.47 39145.90
## 698 699 700 701 702
## 33128.22 29893.87 28395.42 33823.93 34781.81
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8243
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 7.604798 0.5064645 3.407559
## t2* 1977.696325 24.2632003 231.692657
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 3.157064 7.771773 14.2939
## 2 lag_depvar 1644.430551 1987.951911 2402.6800Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Apr 22 00:39:48 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Apr 22 00:39:55 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Apr 22 00:40:02 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Apr 22 00:40:10 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Apr 22 00:40:17 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Apr 22 00:40:24 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Apr 22 00:40:31 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Apr 22 00:40:38 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Apr 22 00:40:45 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Apr 22 00:40:52 2024
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 5.516333 | 5.195333 | 5.410333 | 5.849167 | 6.4088462 |
| Comida | 288.100000 | 366.009167 | 310.278417 | 317.896583 | 341.6904808 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 74.633333 | 38.104750 | 47.072333 | 29.523000 | 35.5299231 |
| Enceres | 38.339333 | 18.259750 | 20.086417 | 14.801167 | 24.5984615 |
| Farmacia | 0.000000 | 4.733250 | 1.831667 | 13.996083 | 7.9840962 |
| Gas/Bencina | 23.665667 | 35.219333 | 44.325000 | 13.583667 | 27.7886346 |
| Diosi | 29.350333 | 55.804250 | 31.180667 | 52.687833 | 42.4919231 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 14.340167 | 5.2830577 |
| Electrodomésticos/ Mantención casa | 20.000000 | 0.000000 | 3.944000 | 56.595000 | 17.1051538 |
| VTR | 21.990000 | 12.829167 | 25.156667 | 19.086917 | 19.2730385 |
| Netflix | 5.565667 | 4.555500 | 7.151583 | 7.028750 | 6.5479615 |
| Otros | 0.000000 | 0.000000 | 3.151083 | 0.000000 | 0.7271731 |
| Total | 507.160667 | 540.710500 | 499.588167 | 545.388333 | 535.4287500 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2321, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-05-09 00:04:58 sería de: 37.727 pesos// Percentil 95% más alto proyectado: 40.830,8
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 37314.39 | 37312.97 |
| Lo.80 | 37326.45 | 37324.65 |
| Point.Forecast | 37726.90 | 38784.71 |
| Hi.80 | 39482.91 | 43631.32 |
| Hi.95 | 40444.95 | 46196.96 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.2428 1018.1513
## s.e. 0.1280 27.5653
##
## sigma^2 = 28060: log likelihood = -404.49
## AIC=814.99 AICc=815.4 BIC=821.37paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2061 694.2043 10.2795
## s.e. 0.1300 248.5873 7.8333
##
## sigma^2 = 27809: log likelihood = -403.68
## AIC=815.37 AICc=816.07 BIC=823.88forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 775.9811 | 679.7095 | 726.3631 |
| Lo.80 | 891.5957 | 796.8561 | 810.9225 |
| Point.Forecast | 1109.9967 | 1018.1513 | 998.4389 |
| Hi.80 | 1328.3978 | 1239.4465 | 1278.9932 |
| Hi.95 | 1444.0124 | 1356.5931 | 1458.1362 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))